{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import h5py\n",
    "import pickle\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_hist():\n",
    "    frames = []\n",
    "    for day in range(2, 4):\n",
    "        hist_file = h5py.File(\"./data/hist/20170102.h5\", \"r\")\n",
    "        for key in hist_file.keys():\n",
    "            plt.imshow(hist_file[key])\n",
    "            plt.show()\n",
    "            frames.append(hist_file[key][:])\n",
    "    return frames\n",
    "    \n",
    "data = get_hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2 ... 0 0 0]\n",
      " [0 1 2 ... 0 0 0]\n",
      " [0 1 2 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_inputs(real_size, noise_size):\n",
    "    real_img = tf.placeholder(tf.float32, [None, real_size]\n",
    "        , name='real_img')\n",
    "    noise_img = tf.placeholder(tf.float32, [None, noise_size]\n",
    "        , name='noise_img')\n",
    "    return real_img, noise_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_generator(noise_img, n_units, out_dim, reuse=False, alpha=0.01):\n",
    "    with tf.variable_scope(\"generator\", reuse=reuse):\n",
    "        # hidden layer\n",
    "        hidden1 = tf.layers.dense(noise_img, n_units)\n",
    "        # leaky ReLU\n",
    "        hidden1 = tf.maximum(alpha * hidden1, hidden1)\n",
    "        # dropout\n",
    "        hidden1 = tf.layers.dropout(hidden1, rate=0.2)\n",
    "        \n",
    "        # logits & outputs\n",
    "        logits = tf.layers.dense(hidden1, out_dim)\n",
    "        outputs = tf.tanh(logits)\n",
    "        \n",
    "        return logits, outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_discriminator(img, n_units, reuse=False, alpha=0.01):\n",
    "    with tf.variable_scope(\"discriminator\", reuse=reuse):\n",
    "        # hidden layer\n",
    "        hidden1 = tf.layers.dense(img, n_units)\n",
    "        hidden1 = tf.maximum(alpha * hidden1, hidden1)\n",
    "\n",
    "        #logits & outputs\n",
    "        logits = tf.layers.dense(hidden1, 1)\n",
    "        outputs = tf.sigmoid(logits)\n",
    "    \n",
    "    return logits, outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image size\n",
      "5742\n"
     ]
    }
   ],
   "source": [
    "img_size = data[0].shape[0] * data[0].shape[1]\n",
    "print(\"image size\")\n",
    "print(img_size)\n",
    "\n",
    "noise_size = 20\n",
    "g_units = 16\n",
    "d_units = 16\n",
    "alpha = 0.01\n",
    "learning_rate = 0.001\n",
    "smooth = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "real_img, noise_img = get_inputs(img_size, noise_size)\n",
    "\n",
    "g_logits, g_outputs = get_generator(noise_img, g_units, img_size)\n",
    "\n",
    "d_logits_real, d_outputs_real = get_discriminator(real_img, d_units)\n",
    "d_logits_fake, d_outputs_fake = get_discriminator(g_outputs, d_units, reuse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_loss_real = tf.reduce_mean(\n",
    "    tf.nn.sigmoid_cross_entropy_with_logits(\n",
    "        logits=d_logits_real,\n",
    "        labels=tf.ones_like(d_logits_real))*(1-smooth))\n",
    "\n",
    "d_loss_fake = tf.reduce_mean(\n",
    "    tf.nn.sigmoid_cross_entropy_with_logits(\n",
    "        logits=d_logits_fake,\n",
    "        labels=tf.zeros_like(d_logits_fake)))\n",
    "\n",
    "d_loss = tf.add(d_loss_real, d_loss_fake)\n",
    "\n",
    "g_loss = tf.reduce_mean(\n",
    "    tf.nn.sigmoid_cross_entropy_with_logits(\n",
    "        logits=d_logits_fake,\n",
    "        labels=tf.ones_like(d_logits_fake))*(1-smooth))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_vars = tf.trainable_variables()\n",
    "\n",
    "g_vars = [var for var in train_vars if var.name.startswith(\"generator\")]\n",
    "d_vars = [var for var in train_vars if var.name.startswith(\"discriminator\")]\n",
    "\n",
    "d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)\n",
    "g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "for batch in data:\n",
    "    batch_images = batch.reshape((1, batch.shape[0]*batch.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100... Discriminator Loss: 1.1967(Real: 0.0000 + Fake: 1.1967)... Generator Loss: 0.3238\n",
      "Epoch 2/100... Discriminator Loss: 2.9725(Real: 0.0000 + Fake: 2.9725)... Generator Loss: 0.0473\n",
      "Epoch 3/100... Discriminator Loss: 2.4045(Real: 0.0000 + Fake: 2.4045)... Generator Loss: 0.0852\n",
      "Epoch 4/100... Discriminator Loss: 3.0879(Real: 0.0000 + Fake: 3.0879)... Generator Loss: 0.0420\n",
      "Epoch 5/100... Discriminator Loss: 1.6792(Real: 0.0000 + Fake: 1.6792)... Generator Loss: 0.1858\n",
      "Epoch 6/100... Discriminator Loss: 0.5372(Real: 0.0000 + Fake: 0.5372)... Generator Loss: 0.7903\n",
      "Epoch 7/100... Discriminator Loss: 0.0627(Real: 0.0000 + Fake: 0.0627)... Generator Loss: 2.5201\n",
      "Epoch 8/100... Discriminator Loss: 0.8964(Real: 0.0000 + Fake: 0.8964)... Generator Loss: 0.4719\n",
      "Epoch 9/100... Discriminator Loss: 2.2609(Real: 0.0000 + Fake: 2.2609)... Generator Loss: 0.0991\n",
      "Epoch 10/100... Discriminator Loss: 1.2153(Real: 0.0000 + Fake: 1.2153)... Generator Loss: 0.3167\n",
      "Epoch 11/100... Discriminator Loss: 0.7127(Real: 0.0000 + Fake: 0.7127)... Generator Loss: 0.6066\n",
      "Epoch 12/100... Discriminator Loss: 0.8227(Real: 0.0000 + Fake: 0.8227)... Generator Loss: 0.5206\n",
      "Epoch 13/100... Discriminator Loss: 0.6246(Real: 0.0000 + Fake: 0.6246)... Generator Loss: 0.6900\n",
      "Epoch 14/100... Discriminator Loss: 0.5952(Real: 0.0000 + Fake: 0.5952)... Generator Loss: 0.7216\n",
      "Epoch 15/100... Discriminator Loss: 0.5156(Real: 0.0000 + Fake: 0.5156)... Generator Loss: 0.8183\n",
      "Epoch 16/100... Discriminator Loss: 0.4047(Real: 0.0000 + Fake: 0.4047)... Generator Loss: 0.9902\n",
      "Epoch 17/100... Discriminator Loss: 0.5760(Real: 0.0000 + Fake: 0.5760)... Generator Loss: 0.7433\n",
      "Epoch 18/100... Discriminator Loss: 0.0456(Real: 0.0000 + Fake: 0.0456)... Generator Loss: 2.7996\n",
      "Epoch 19/100... Discriminator Loss: 4.6800(Real: 0.0000 + Fake: 4.6800)... Generator Loss: 0.0084\n",
      "Epoch 20/100... Discriminator Loss: 2.6793(Real: 0.0000 + Fake: 2.6793)... Generator Loss: 0.0640\n",
      "Epoch 21/100... Discriminator Loss: 0.7340(Real: 0.0000 + Fake: 0.7340)... Generator Loss: 0.5885\n",
      "Epoch 22/100... Discriminator Loss: 2.5844(Real: 0.0000 + Fake: 2.5844)... Generator Loss: 0.0706\n",
      "Epoch 23/100... Discriminator Loss: 0.4110(Real: 0.0000 + Fake: 0.4110)... Generator Loss: 0.9788\n",
      "Epoch 24/100... Discriminator Loss: 0.6152(Real: 0.0000 + Fake: 0.6152)... Generator Loss: 0.6999\n",
      "Epoch 25/100... Discriminator Loss: 0.7583(Real: 0.0000 + Fake: 0.7583)... Generator Loss: 0.5688\n",
      "Epoch 26/100... Discriminator Loss: 0.2051(Real: 0.0000 + Fake: 0.2051)... Generator Loss: 1.5166\n",
      "Epoch 27/100... Discriminator Loss: 3.5071(Real: 0.0000 + Fake: 3.5071)... Generator Loss: 0.0274\n",
      "Epoch 28/100... Discriminator Loss: 6.5929(Real: 0.0000 + Fake: 6.5929)... Generator Loss: 0.0012\n",
      "Epoch 29/100... Discriminator Loss: 7.0510(Real: 0.0000 + Fake: 7.0510)... Generator Loss: 0.0008\n",
      "Epoch 30/100... Discriminator Loss: 0.4728(Real: 0.0000 + Fake: 0.4728)... Generator Loss: 0.8786\n",
      "Epoch 31/100... Discriminator Loss: 0.1890(Real: 0.0000 + Fake: 0.1890)... Generator Loss: 1.5831\n",
      "Epoch 32/100... Discriminator Loss: 1.2638(Real: 0.0000 + Fake: 1.2638)... Generator Loss: 0.2989\n",
      "Epoch 33/100... Discriminator Loss: 0.7826(Real: 0.0000 + Fake: 0.7826)... Generator Loss: 0.5499\n",
      "Epoch 34/100... Discriminator Loss: 0.7840(Real: 0.0000 + Fake: 0.7840)... Generator Loss: 0.5489\n",
      "Epoch 35/100... Discriminator Loss: 6.1174(Real: 0.0000 + Fake: 6.1174)... Generator Loss: 0.0020\n",
      "Epoch 36/100... Discriminator Loss: 0.1202(Real: 0.0000 + Fake: 0.1202)... Generator Loss: 1.9603\n",
      "Epoch 37/100... Discriminator Loss: 2.5530(Real: 0.0000 + Fake: 2.5530)... Generator Loss: 0.0729\n",
      "Epoch 38/100... Discriminator Loss: 1.6268(Real: 0.0000 + Fake: 1.6268)... Generator Loss: 0.1970\n",
      "Epoch 39/100... Discriminator Loss: 0.7634(Real: 0.0000 + Fake: 0.7634)... Generator Loss: 0.5648\n",
      "Epoch 40/100... Discriminator Loss: 0.4055(Real: 0.0000 + Fake: 0.4055)... Generator Loss: 0.9886\n",
      "Epoch 41/100... Discriminator Loss: 4.1778(Real: 0.0000 + Fake: 4.1778)... Generator Loss: 0.0139\n",
      "Epoch 42/100... Discriminator Loss: 2.6988(Real: 0.0000 + Fake: 2.6988)... Generator Loss: 0.0627\n",
      "Epoch 43/100... Discriminator Loss: 0.0336(Real: 0.0000 + Fake: 0.0336)... Generator Loss: 3.0697\n",
      "Epoch 44/100... Discriminator Loss: 6.4621(Real: 0.0000 + Fake: 6.4621)... Generator Loss: 0.0014\n",
      "Epoch 45/100... Discriminator Loss: 0.6290(Real: 0.0000 + Fake: 0.6290)... Generator Loss: 0.6855\n",
      "Epoch 46/100... Discriminator Loss: 0.0653(Real: 0.0000 + Fake: 0.0653)... Generator Loss: 2.4854\n",
      "Epoch 47/100... Discriminator Loss: 1.2432(Real: 0.0000 + Fake: 1.2432)... Generator Loss: 0.3063\n",
      "Epoch 48/100... Discriminator Loss: 0.0308(Real: 0.0000 + Fake: 0.0308)... Generator Loss: 3.1449\n",
      "Epoch 49/100... Discriminator Loss: 0.0005(Real: 0.0000 + Fake: 0.0005)... Generator Loss: 6.9114\n",
      "Epoch 50/100... Discriminator Loss: 0.2443(Real: 0.0000 + Fake: 0.2443)... Generator Loss: 1.3762\n",
      "Epoch 51/100... Discriminator Loss: 0.8801(Real: 0.0000 + Fake: 0.8801)... Generator Loss: 0.4821\n",
      "Epoch 52/100... Discriminator Loss: 0.7266(Real: 0.0000 + Fake: 0.7266)... Generator Loss: 0.5947\n",
      "Epoch 53/100... Discriminator Loss: 1.4091(Real: 0.0000 + Fake: 1.4091)... Generator Loss: 0.2522\n",
      "Epoch 54/100... Discriminator Loss: 5.5576(Real: 0.0000 + Fake: 5.5576)... Generator Loss: 0.0035\n",
      "Epoch 55/100... Discriminator Loss: 0.9136(Real: 0.0000 + Fake: 0.9136)... Generator Loss: 0.4613\n",
      "Epoch 56/100... Discriminator Loss: 1.7574(Real: 0.0000 + Fake: 1.7574)... Generator Loss: 0.1704\n",
      "Epoch 57/100... Discriminator Loss: 1.9888(Real: 0.0000 + Fake: 1.9888)... Generator Loss: 0.1325\n",
      "Epoch 58/100... Discriminator Loss: 2.8955(Real: 0.0000 + Fake: 2.8955)... Generator Loss: 0.0512\n",
      "Epoch 59/100... Discriminator Loss: 2.3359(Real: 0.0000 + Fake: 2.3359)... Generator Loss: 0.0916\n",
      "Epoch 60/100... Discriminator Loss: 0.0914(Real: 0.0000 + Fake: 0.0914)... Generator Loss: 2.1944\n",
      "Epoch 61/100... Discriminator Loss: 0.0204(Real: 0.0000 + Fake: 0.0204)... Generator Loss: 3.5123\n",
      "Epoch 62/100... Discriminator Loss: 0.8101(Real: 0.0000 + Fake: 0.8101)... Generator Loss: 0.5296\n",
      "Epoch 63/100... Discriminator Loss: 1.9008(Real: 0.0000 + Fake: 1.9008)... Generator Loss: 0.1457\n",
      "Epoch 64/100... Discriminator Loss: 1.3245(Real: 0.0000 + Fake: 1.3245)... Generator Loss: 0.2782\n",
      "Epoch 65/100... Discriminator Loss: 0.0368(Real: 0.0000 + Fake: 0.0368)... Generator Loss: 2.9896\n",
      "Epoch 66/100... Discriminator Loss: 0.3267(Real: 0.0000 + Fake: 0.3267)... Generator Loss: 1.1498\n",
      "Epoch 67/100... Discriminator Loss: 0.7950(Real: 0.0000 + Fake: 0.7950)... Generator Loss: 0.5407\n",
      "Epoch 68/100... Discriminator Loss: 0.1614(Real: 0.0000 + Fake: 0.1614)... Generator Loss: 1.7132\n",
      "Epoch 69/100... Discriminator Loss: 0.9093(Real: 0.0000 + Fake: 0.9093)... Generator Loss: 0.4640\n",
      "Epoch 70/100... Discriminator Loss: 0.8895(Real: 0.0000 + Fake: 0.8895)... Generator Loss: 0.4762\n",
      "Epoch 71/100... Discriminator Loss: 1.6270(Real: 0.0000 + Fake: 1.6270)... Generator Loss: 0.1969\n",
      "Epoch 72/100... Discriminator Loss: 0.4307(Real: 0.0000 + Fake: 0.4307)... Generator Loss: 0.9450\n",
      "Epoch 73/100... Discriminator Loss: 0.3132(Real: 0.0000 + Fake: 0.3132)... Generator Loss: 1.1820\n",
      "Epoch 74/100... Discriminator Loss: 0.3765(Real: 0.0000 + Fake: 0.3765)... Generator Loss: 1.0434\n",
      "Epoch 75/100... Discriminator Loss: 0.1060(Real: 0.0000 + Fake: 0.1060)... Generator Loss: 2.0669\n",
      "Epoch 76/100... Discriminator Loss: 1.0365(Real: 0.0000 + Fake: 1.0365)... Generator Loss: 0.3942\n",
      "Epoch 77/100... Discriminator Loss: 0.4836(Real: 0.0000 + Fake: 0.4836)... Generator Loss: 0.8627\n",
      "Epoch 78/100... Discriminator Loss: 1.2667(Real: 0.0000 + Fake: 1.2667)... Generator Loss: 0.2978\n",
      "Epoch 79/100... Discriminator Loss: 0.6565(Real: 0.0000 + Fake: 0.6565)... Generator Loss: 0.6581\n",
      "Epoch 80/100... Discriminator Loss: 0.0188(Real: 0.0000 + Fake: 0.0188)... Generator Loss: 3.5858\n",
      "Epoch 81/100... Discriminator Loss: 0.6900(Real: 0.0000 + Fake: 0.6900)... Generator Loss: 0.6267\n",
      "Epoch 82/100... Discriminator Loss: 0.3421(Real: 0.0000 + Fake: 0.3421)... Generator Loss: 1.1150\n",
      "Epoch 83/100... Discriminator Loss: 0.0002(Real: 0.0000 + Fake: 0.0002)... Generator Loss: 7.7488\n",
      "Epoch 84/100... Discriminator Loss: 0.2711(Real: 0.0000 + Fake: 0.2711)... Generator Loss: 1.2940\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85/100... Discriminator Loss: 0.4428(Real: 0.0000 + Fake: 0.4428)... Generator Loss: 0.9251\n",
      "Epoch 86/100... Discriminator Loss: 0.0785(Real: 0.0000 + Fake: 0.0785)... Generator Loss: 2.3250\n",
      "Epoch 87/100... Discriminator Loss: 0.0237(Real: 0.0000 + Fake: 0.0237)... Generator Loss: 3.3804\n",
      "Epoch 88/100... Discriminator Loss: 4.7191(Real: 0.0000 + Fake: 4.7191)... Generator Loss: 0.0081\n",
      "Epoch 89/100... Discriminator Loss: 2.2880(Real: 0.0000 + Fake: 2.2880)... Generator Loss: 0.0963\n",
      "Epoch 90/100... Discriminator Loss: 0.0980(Real: 0.0000 + Fake: 0.0980)... Generator Loss: 2.1339\n",
      "Epoch 91/100... Discriminator Loss: 0.5748(Real: 0.0000 + Fake: 0.5748)... Generator Loss: 0.7447\n",
      "Epoch 92/100... Discriminator Loss: 0.0280(Real: 0.0000 + Fake: 0.0280)... Generator Loss: 3.2293\n",
      "Epoch 93/100... Discriminator Loss: 1.8867(Real: 0.0000 + Fake: 1.8867)... Generator Loss: 0.1479\n",
      "Epoch 94/100... Discriminator Loss: 0.5899(Real: 0.0000 + Fake: 0.5899)... Generator Loss: 0.7275\n",
      "Epoch 95/100... Discriminator Loss: 0.3655(Real: 0.0000 + Fake: 0.3655)... Generator Loss: 1.0654\n",
      "Epoch 96/100... Discriminator Loss: 1.9562(Real: 0.0000 + Fake: 1.9562)... Generator Loss: 0.1372\n",
      "Epoch 97/100... Discriminator Loss: 1.2790(Real: 0.0000 + Fake: 1.2790)... Generator Loss: 0.2935\n",
      "Epoch 98/100... Discriminator Loss: 1.5432(Real: 0.0000 + Fake: 1.5432)... Generator Loss: 0.2164\n",
      "Epoch 99/100... Discriminator Loss: 1.0279(Real: 0.3349 + Fake: 0.6930)... Generator Loss: 0.6239\n",
      "Epoch 100/100... Discriminator Loss: 2.5397(Real: 0.0000 + Fake: 2.5397)... Generator Loss: 0.0740\n"
     ]
    }
   ],
   "source": [
    "batch_size = 1\n",
    "epochs = 100\n",
    "n_sample = 2\n",
    "\n",
    "samples = []\n",
    "losses = []\n",
    "saver = tf.train.Saver(var_list = g_vars)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for e in range(epochs):\n",
    "        for batch in data:\n",
    "            batch_images = batch.reshape((batch_size, batch.shape[0]*batch.shape[1]))\n",
    "            batch_images = batch_images*2 - 1\n",
    "            \n",
    "            batch_noise = np.random.uniform(-1, 1, size=(batch_size, noise_size))\n",
    "            \n",
    "            sess.run(d_train_opt, feed_dict={real_img:batch_images, noise_img:batch_noise})\n",
    "            sess.run(g_train_opt, feed_dict={noise_img: batch_noise})\n",
    "        \n",
    "        train_loss_d = sess.run(d_loss,\n",
    "                               feed_dict={real_img: batch_images,\n",
    "                                          noise_img: batch_noise})\n",
    "        \n",
    "        train_loss_d_real = sess.run(d_loss_real,\n",
    "                                    feed_dict={real_img: batch_images,\n",
    "                                              noise_img: batch_noise})\n",
    "        \n",
    "        train_loss_d_fake = sess.run(d_loss_fake,\n",
    "                                    feed_dict={real_img: batch_images,\n",
    "                                              noise_img: batch_noise})\n",
    "        \n",
    "        train_loss_g = sess.run(g_loss,\n",
    "                               feed_dict={noise_img: batch_noise})\n",
    "        \n",
    "        print(\"Epoch {}/{}...\".format(e+1, epochs),\n",
    "             \"Discriminator Loss: {:.4f}(Real: {:.4f} + Fake: {:.4f})...\"\n",
    "             .format(train_loss_d, train_loss_d_real, train_loss_d_fake),\n",
    "             \"Generator Loss: {:.4f}\".format(train_loss_g))\n",
    "        losses.append((train_loss_d, train_loss_d_real, train_loss_d_fake, train_loss_g))\n",
    "        \n",
    "        sample_noise = np.random.uniform(-1, 1, size=(n_sample, noise_size))\n",
    "        gen_samples = sess.run(get_generator(noise_img, g_units, img_size, reuse=True),\n",
    "                              feed_dict={noise_img: sample_noise})\n",
    "        samples.append(gen_samples)\n",
    "        saver.save(sess, './checkpoints/generator.ckpt')\n",
    "        \n",
    "    with open('train_samples.pkl', 'wb') as f:\n",
    "        pickle.dump(samples, f)"
   ]
  }
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